1 1. Representing and Parameterizing Agent Behaviors Jan Allbeck and Norm Badler 연세대학교 컴퓨터과학과 로봇 공학 특강 학기 유 지 오

2 Agenda sub-title Introduction Control vs. Autonomy AI-Level Representation Network Simulation Parameterized Action Representation –PAR Architecture –Action Representation –Object Representation PAR for Agent Modeling –Personality and Emotions –EMOTE for Displaying Affect Interfaces to Representations Conclusions and Future Research

3 Introduction The world is complex  difficult to represent… In order to create an interactive world that meets natural expectations  substantial amount of computer S/W Engineering is required –Graphical depictions, motion models or generators, collision detection and avoidance, communication or synchronization channels, planning and navigation, cognitive modeling, psychosocial and physiological modeling … An action representation is IMPORTANT!! In this chapter… –Outline some thing to consider when adopting an action representation –Present a representation, Parameterized Action Representation (PAR)

4 Control vs. Autonomy Control –Key-frame animation –Detailed control over the movement of the characters –A time consuming process, required a large storage, specific to a character –Cannot be altered to context  Difficult to… Interact with objects and other agents Create transitions between motions Alter the expression of the motion to new context Autonomy –Decrease the data, enable context-sensitive actions –Use Inverse kinematics –Motion capture –Example) Jack, DI-Guy (Human Simulation) … Low-level motion representations

5 AI-Level Representation High-level representations –Can vary in their purpose and their semantics Communicative or conversational Agents –Mechanisms to synchronize facial expressions with speech –Extract semantic information from text Perform autonomously in a virtual world –Concentrate on an agent’s interactions and autonomy Planning for characters in virtual environments –Require representations of the state of the environment (dynamic)  Object must also be represented Cognitive and social modeling –Emotional states, goals, motivations, and more…

6 Network Simulations Design dimensions for distributed or networked simulations –Bandwidth, synchronization, agent autonomy, agent control, latency, visualization, interfaces… –Trade off Ex) Minimize bandwidth vs. maximize control Packets describing agent actions must be formulated, sent, received, and interpreted Increasing the autonomy  decreasing in necessary bandwidth –Frame-by-frame joint angle vs. string “enter the building” “enter the building + carefully + through the blue door” –Modification the detailed joint or motion capture data is IMPOSSSIBLE!! –If the actions are suitably parameterized  POSSIBLE!!

7 Parameterized Action Representation PAR allows an agent to act, plan, and reason A knowledge base and intermediary between natural language and animation Specify (parameterize) the agent –Any relevant objects, information about paths, locations, manners, and purposes PAR

8 PAR Architecture Actionary  stores uninstantiated PARs (UPARs) Agent Process  create instantiated PARs (IPARs) –Consider emotion, personality factors, current state of the world Motion Generators  simply replay stored joint angle data or alter this data for context or affect PAR

9 Action Representation Include fields for low-level animation concepts –Kinematics, dynamics, … Participants –Object or other agents involved in the action or can be affected by it Applicability conditions –True  can perform the action Preparatory specifications –A list of statements Termination conditions –A list of conditions which when satisfied indicate the completion of the action PAR

10 Object Representation Stored Actionary Virtual world created  retrieve object from the actionary  instantiated  placed  updated throughout the simulation Associated with a graphical model in a scene graph Many of the fields can be filled in as the simulation begins –Ex) bounding volume Help orient actions that involve objects PAR

11 Funge et al[19], hierarchy of computer graphics modeling PAR for Agent Modeling PAR and PARSYS enable each level –Geometric  PAR represents and PARSYS automatically recognizes –Kinematics and dynamics (physical)  explicitly represented in PAR –Behavioral component  World model + agent processes + motion generators in PARSYS –Cognitive modeling  PARSYS contains mechanisms for planning and also filtering and prioritizing the actions Individualizing the agent Use conditions (Actionary)

12 Personality and Emotions Personality  OCEAN –“Big Five” Openness Conscientiousness Extroversion Agreeableness Neuroticism Emotion  OCC –Emotion are generated through the agent’s construal of and reaction to the consequence of events, actions of agents, aspects of objects PAR for Agent Modeling

13 EMOTE for Displaying Affect EMOTE system –Based on movement observation science –Laban Movement Analysis (LMA)  Effort and Shape PAR for Agent Modeling

14 EMOTE Example Hitting a balloon –Differing EMOTE setting PAR for Agent Modeling

15 EMOTE and OCEAN linkage Future work in EMOTE system and the motion quality recognizer –Train the system to correlate captured motions with actor affect, behavior, mood, and intent PAR for Agent Modeling

16 Interfaces to Representations Basic scripting languages –Create outline to perform … Specified action Specified time Drag-and-drop creation applications –For virtual environments Natural language

17 Conclusions and Future Research An action representation –Autonomy and control –Minimize data storage –Provide semantic for planning Level of detail –Nearby action: Inverse kinematics –Further distance: replaying motion capture data –Cognitive representation for conveying action information between agents Flexible representation –Different types of information Trade-off –Parameterization specificity vs. program complexity Future work –PAR to XML representation –EMOTE parameterization  models of personality and emotion –Natural language interface